Founding Engineer

Location: New York City or San Fran - 4 days per week onsite, 1 day remote

Work Style: In-office, 5 days per week

Employment Type: Full-time

Experience: 6–10 years Focus: Backend Engineering, Data Modeling, Knowledge Graphs, Agentic Workflows, Chemical Manufacturing About Our Client Our client is building an AI-native platform for the chemical manufacturing industry. The company is solving a surprisingly unsolved problem in a massive market: helping manufacturers and distributors understand which products from their portfolio they can sell into each account. In chemical manufacturing, sales teams often manage hundreds or thousands of SKUs across tens of thousands of accounts. Figuring out what to sell where requires deep research, industry experience, customer conversations, and chemistry knowledge. Our client is changing that by combining agent-powered research workflows with a proprietary dataset of U.S. manufacturers. Their platform helps customers understand what each manufacturer makes, which chemistries go into those products, and how that maps to a specific customer’s portfolio. The company has strong traction, including significant revenue growth, customer expansion, and renewals from early customers across major chemical distributors and manufacturers.

About the Role: Our client is hiring a Senior Backend Engineer to own the backend systems, data architecture, and core data model that power the product. This person will work directly with the two co-founders every day. There is no PM layer. You’ll hear customer problems firsthand, translate them into system requirements, and own the work from design through shipping and production behavior. The company’s product runs on a growing body of data about manufacturers, facilities, products, chemistries, and people. Your job will be to design the systems that keep this data correct, consistent, queryable, and useful as the platform scales. This is a high-ownership role for someone who wants to build the foundational data layer of a company.

What You’ll Do: Design and build the backend systems that power an AI-native platform for chemical manufacturing Own the architecture and data model behind manufacturers, facilities, products, chemistries, accounts, and people Build a knowledge graph that turns fragmented industry data into a coherent, queryable system Design systems that support agent-powered research workflows Structure data so AI agents can read, write, validate, and improve it over time Build and improve entity resolution, deduplication, reconciliation, and data quality workflows Make key architecture decisions around what to build now, what to defer, and how to scale pragmatically Build resilient systems using queues, retries, idempotency, observability, and strong operational practices Work directly with customers to understand workflows, pain points, and product opportunities Partner closely with the co-founders to define product direction and technical priorities Ship quickly in a lean, high-agency environment without unnecessary process or handoffs

What We’re Looking For: 6–10 years of professional software engineering experience Deep relational database expertise, preferably with PostgreSQL Strong TypeScript or Python experience Experience designing canonical data models that have evolved through real-world change Experience handling new data sources, new entity types, merges, splits, and changing business requirements Experience building or owning entity resolution, deduplication, matching, or reconciliation systems Strong understanding of deterministic rules, probabilistic matching, and human-in-the-loop review tradeoffs Experience building resilient backend systems with queues, retries, idempotency, and observability Pragmatic judgment around scale and the ability to explain tradeoffs clearly Comfort using AI tools as a core part of the engineering workflow Curiosity about how data should be structured for agents to work with it effectively Desire to work directly with customers and use customer conversations to inform technical decisions Excitement about working in person with the team in New York City 5 days per week Bonus Experience Experience building knowledge graphs, data platforms, or graph-like relational models Experience with agentic workflows, AI research agents, or AI-native data products Experience working with messy real-world industrial, manufacturing, supply chain, or B2B data Experience building tools for sales intelligence, account research, market mapping, or portfolio analysis Experience at an early-stage startup with high ownership and direct founder collaboration Experience designing systems where AI agents are primary users or contributors to the data layer Why This Opportunity Own the data architecture behind a fast-growing AI-native company Build the foundational data layer the business is being built on Work directly with founders and customers every day Help create a knowledge graph for a massive, underserved industry Build systems where agents are primary users of the platform Join a lean team with strong traction, customer renewals, and rapid growth Make architecture decisions at the stage where they compound the most Work on a real industrial problem with significant commercial demand

Ideal Candidate Profile: The ideal candidate is a senior backend engineer with deep data modeling instincts and strong database fundamentals. They have built systems where data correctness, consistency, and evolution matter. They understand entity resolution, reconciliation, and the tradeoffs between clean architecture and pragmatic shipping. This person is excited to build the data foundation for an AI-native platform, work directly with customers, and own backend architecture at an early-stage company with real traction.